import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from joblib import load

class NN(object):
    
    def __init__(self):
        self.loaded_model = tf.keras.models.load_model('NN/my_model.keras')
        self.scaler = load('NN/scaler.joblib')
    

    def get_hourly_trend(self):
        n_steps = 7 # 长度七天
        df_pivot = pd.read_csv('./NN/scenic_data.csv')
        latest_data = df_pivot.iloc[-n_steps:].values # 取最后七天数据
        latest_data = latest_data.reshape(1, n_steps, latest_data.shape[1])
        
        predicted = self.loaded_model.predict(latest_data)
        predicted_counts = self.scaler.inverse_transform(predicted) # 反归一化
        predicted_counts[predicted_counts  < 0] = 0 # 修正负数
        
        hourly_trend = predicted_counts[0].astype(int).tolist()
        print(hourly_trend)
        
if __name__ == '__main__':
    n = NN()
    n.get_hourly_trend()
    